Validated predictive modelling of the environmental resistome

Abstract

Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

Figure 1

(a) Map illustrating the Thames Watershed in Oxfordshire, sampling sites and WWTPs. Site names are an extension of a previously defined naming system. (b) Map illustrating the Thames Watershed in Oxfordshire with associated land covers extracted from the LCM2007 (Morton et al., 2007).

Figure 2

Example of geospatial analyses conducted at sampling sites. Map illustrating distances from all WWTPs within a 10-km buffer, which drained into the sampling site TC1.

Figure 3

Class 1 integron prevalence taken from 13 sites across the Thames river basin at four time points. Class 1 integron prevalence was calculated as the ratio of intI1 genes to 16S rRNA genes expressed as percentage. TC19 was not sampled in February or May, and TC3 was not sampled in May. Error bars are ±s.e.m. of three biological replicates.

Figure 4

Output from model 2, which explains 82.9% of the variation in antibiotic-resistance levels in the River Thames basin. Red line is predicted values, blue line is 95% confidence levels.

Figure 5

Comparison of average predicted values for class 1 integron prevalence in a river from the Midlands based on model 2, compared to observed values for class 1 integron prevalence. Samples were taken from upstream and downstream of a WWTP at 300- and 600-m intervals (Amos et al., 2014a). Error bars for observed are based on ±s.e.m. of three biological replicates.